DoorDash's physical-world complexity — 3 billion diverse deliveries per year across different geographies, merchant types, and order types — gives them an insurmountable data advantage for building autonomy that no outsider can replicate.
Stanley Tang explains that the sheer variety and scale of DoorDash's delivery operations creates proprietary data and operational insights that competitors cannot access. ✦ AI generated
Stanley Tang · No Priors · 2026-07-23 · original ↗
starts at this moment · 17:47
I think one of the things I think people don't realize is just how complicated DoorDash is. I mean, we do what over 3 billion deliveries a year. There are no two deliveries that look the same. All three billion deliveries look look different. Uh, and they all come in all sorts of shapes and sizes and different geographies. like a delivery in downtown San Francisco is completely different than uh a delivery done in Dallas or or or even in Europe or in Helsinki where it's snowing or if you're doing a uh pizza is very different than ice cream like your your dinner is very different than your grocery order which is very different now that we're expanding to retail and and and pharmacy and parcels as well. It's like the diversity of deliveries that happen at DoorDash is so complex that I I think people sometimes don't realize just how nuanced the problem the problem the problem is.
verbatim transcript · starts at 17:47
17:29okay we'll make the model and then like the other stuff will be if not easy at least secondary. This is not my view at all. >> I Yeah, I agree with you there. I mean that's basically your methodology to building the dot form factor. >> I think maybe that approach works in like software land but like for at least for a business like ours like Door Dash
17:52is a physical world business. It's like you know you bring technology into physical world and the physical world is always a lot messier. It's a lot more complicated, a lot more nuanced. Uh, I I think one of the things I think people don't realize is just how complicated Door Dash is. I mean, we do what over 3 billion deliveries a year. There are no two deliveries that look the same. All
18:15three billion deliveries look look different. Uh, and they all come in all sorts of shapes and sizes and different geographies. like a delivery in downtown San Francisco is completely different than uh a delivery done in Dallas or or or even in Europe or in Helsinki where it's snowing or if you're doing a uh pizza is very different than ice cream like your your dinner is very different
18:39than your grocery order which is very different now that we're expanding to retail and and and pharmacy and parcels as well. It's like the diversity of deliveries that happen at Door Dash is so complex that I I think people sometimes don't realize just how nuanced the problem the problem the problem is. And and that's kind of how what we have to solve for at at Door Dash. And I think that's part of the
19:06been been the learning process especially when it comes to like building autonomy or even AI is how do you manage through all that complexity and again it always comes down to like like do you understand the use case and I think we just have such a huge advantage over everyone else because we have something that everyone else doesn't have is it's called Door Dash. We have 10 billion deliveries of data to
19:28extract from. We have uh all these consumers like you know over 40 million consumers ordering every single month like we understand the complexities of how to handle when things go wrong. How to integrate all across all different types of merchants. Like the way you work with a McDonald's or Starbucks is very different than working with a mom and pop sandwich shop. Like a a drive-thru restaurant is again is very